The Midwest ML Symposium (MMLS) aims to convene regional machine
learning researchers for stimulating discussions and debates, to
foster cross-institutional collaboration, and to showcase the
collective talent of machine learning researchers at all career
stages.

Accommodation:
A block of double-occupancy dorm rooms has been reserved on
the University of Illinois at Chicago campus, and will be
offered to MMLS participants, free of charge, on
a first-come, first-served basis. Complimentary shuttle
transportation will also be provided between the UIC and
UChicago campuses in the mornings and evenings.

Registration:
We are grateful to the 400+ current registrants;
we look forward to seeing all of you soon!
Unfortunately, we are at capacity,
and closing registration!

Posters:
In 2017, we were very happy to have ~50 posters;
three of which received best poster prizes.
In 2018, we have roughly as many posters;
we have selected 10 spotlight talks,
and will award three $1000 prizes,
graciously sponsored by 3M!
Please see below for details!

Invited speakers.

Posters.

Thank you to everyone who has submitted posters.
Unfortunately, the deadline has passed and
we have closed poster submission.
Please see the list of accepted posters and spotlights
in the schedule below.

We will still select, from the spotlights,
three best poster prizes
(each accompanied by $1000),
graciously sponsored by 3M!

Schedule.

Wednesday, June 6.

Title.
Metrics, Insights, and Lessons Learned from Operating ML Systems in Advertising.
Chair.
Po-Ling Loh
[+-
Abstract
]
The role of a machine learning researcher usually begins with inventing a
novel algorithm to solve an important problem, and ends with a prototype
and a paper. From an engineering perspective, however, this is just the
beginning. The prototype will be turned into a robust system for
production, and a team will monitor and operate the system throughout its
life time. With probability 1, something will go wrong. What happens next
are the three key steps of system operations: detection, mitigation, and
root cause diagnosis. In this talk, we will discuss lessons learned from
operating machine learning systems in programmatic advertising, including
operational procedures and metrics that we've developed to guard against
these near-certainty situations.

[+-
Bio
]
Alice Zheng is the head of the Machine Learning Optimization team in
Amazon advertising. She specializes in research and development of machine
learning methods, tools, and applications. She recently co-authored an
O'Reilly book on "Feature Engineering for Machine Learning."
Previously, she worked
at Turi/Dato/GraphLab, where she led the Toolkits team and helped with
marketing and user education. She worked as a researcher at Microsoft
Research, Redmond and
as a postdoc at Carnegie Mellon University's Auton Lab
and the Parallel Data Lab.
Alice received B.A.s in Mathematics and Computer
Science and a Ph.D. from U. C. Berkeley
in Prof. Michael Jordan's lab.

Human language is notoriously complex due to the multitude of ways people
can express the same meaning (i.e. paraphrases). I will present our work
on robust machine learning methods for large-scale paraphrasing,
including 1) automatic paraphrase acquisition that exploited
multi-instance learning and deep neural networks for semantics; and 2)
utilizing paraphrases for various natural language generation tasks with
machine translation techniques. I will also show how similar
multi-instance learning models can learn large knowledge bases and
resolve time expressions with limited labeled data.

Wei Xu is an assistant professor of Computer Science and Engineering at the
Ohio State University. Her research lies at the intersections of machine
learning, natural language processing, and social media. She received her PhD
in Computer Science from New York University where she was a MacCracken fellow.
Between 2014 and 2016, she was a postdoctoral researcher at the University of
Pennsylvania. She recently received the NSF CRII Award, CrowdFlower AI for
Everyone Award in addition to funding from DARPA.

Recent algorithmic advances have led to the emergence of provably accurate
algorithms for learning a dictionary of atoms that represent a given
dataset. However, these algorithms are beset with several challenges: high
running time, large memory costs, and susceptibility to missing data. In
this talk, we address these challenges by constructing a family of new
algorithms where the dictionaries themselves obey conciseness assumptions
(such as compositionality, sparsity and/or democracy). We also discuss
implications of our algorithmic techniques for training (shallow)
autoencoder architectures. Joint work with Thanh Nguyen, Raymond Wong, and
Akshay Soni.

Chinmay Hegde is an assistant professor, and Black and Veatch Faculty
Fellow, in Electrical and Computer Engineering at Iowa State University.
His research focuses on developing fast and robust algorithms for machine
learning and statistical signal processing, with applications to imaging
problems. Prior to this, he received his PhD in Electrical and Computer
Engineering at Rice University, and was a Shell-MIT Postdoctoral Associate
in CSAIL at the Massachusetts Institute of Technology. Chinmay is the
recipient of multiple awards, including best paper awards at SPARS and
ICML, the Budd Award for Best Engineering Thesis at Rice University in
2013, the Warren Boast Award for Undergraduate Teaching in 2016, the NSF
CRII Award in 2016, and the NSF CAREER Award in 2018.

ServiceNow was founded on a very simple idea: that work should be easier.
That getting simple stuff done shouldn’t be so hard and complex stuff
should be manageable. It started with IT—creating a System of Action to
streamline and automate unstructured work, eliminating the back and forth
emails, phone calls, and manual processes that waste time, money, and sap
productivity. Today, an entire enterprise—HR, customer service, security,
and beyond—can tap into the power of the Now Platform™ to create a better
experience for employees, users, and customers, and transform the way work
is done.

There is history. Each enterprise has their own unique history. Much of the
history is textual information– emails, notes, FAQs, knowledge bases,
communities, and now chats and conversations. Each enterprise has its own
understanding of textual information being exchanged. Better we get to know
the enterprise language, easier it becomes to make “work easy” for our
customers. To transform the way work is done, resolve issues faster and
increase agent efficiency, ServiceNow® uses machine learning to
automatically categorize, route, and prioritize issues primarily from text.
It learns from patterns in your historical data, becoming increasingly
accurate in its predictive recommendations. Operationalizing this learning
and applying it scalably out of the box for each of our customer one at a
time is what we define as Enterprise AI.

Debu Chatterjee heads the Machine Learning, ArtificiaI Intelligence, and Analytics Product Engineering
Organisation of ServiceNow. Debu joined ServiceNow in Jan 2017 when his company DxContinuum was
acquired by ServiceNow to seed ServiceNow intelligent automation roadmap. The fully integrated
DxContinuum Fathom engine in the NOW platform now powers the Agent Intelligence product of
ServiceNow. He led the acquisition of Parlo.io – a Natural Language Understanding engine to deliver
conversational AI agents and enterprise language modeling.

Earlier, in 2012, he founded a business decisions company DxContinuum specializing in operationalizing
predictive business outcomes. As the CEO of the company, he brought the transformative power of
predictive analytics to B2B sales and marketing operations for a roster of customers like Adobe, Cisco
and VMWare. The patented, machine- learning software platform accelerated the model generation and
delivery for a variety of supervised machine learning use cases.

Prior, Debu has demonstrated successful technology leadership in diverse settings and scale. He was a
key member of Oracle's flagship product, the database from version 6 to version 11 in various capacities.
Every SQL access in Oracle goes through the code he has written. He led Informatica's PowerCenter and
Connectors group delivering the flagship V9 release, and subsequently the Health Care Fraud Analytics
team at FICO. His enterprise software development experience spans 30 years in the areas of machine
learning, predictive analytics, metadata services, distributed systems, databases, big data, systems
management and fraud detection.

He has a BS in CS from IIT Kharagpur, a MS in Computer Science from UNC Chapel Hill, and an MBA from
Wharton. He holds 23 patents, and has filed numerous others. He is a technical advisory member at
Benhamou Global Ventures, and a member of BOV and GEAB at UNC Chapel Hill. He also contributes his
time to various Silicon Valley professional and mentorship organizations.

Map making on a global scale is a monumental undertaking.
It requires extensive data collection, feature detection, aggregation of
tens of thousands of heterogeneous sources, validation, and continuous
monitoring to adapt to changes in real-time. Machine learning research at
HERE Technologies is not only focused on solving these core map making
problems, but also how to leverage this location data to provide services
across many different industries.

Ian Endres is a Lead Research Engineer at HERE Technologies.
His research focuses on applying computer vision and machine learning to
feature detection from a range of sensors, and automatically deriving
detailed geospatial maps from those features. He received his BS and PhD
in Computer Science from University of Illinois at Urbana-Champaign.

This talk will discuss the opportunities for Artificial Intelligence
(AI) in a materials company, 3M. 3M has a diverse portfolio of products
(50,000+), in markets ranging from medical informatics to worker safety
to air quality and more. This diversity provides unique opportunities
for Data Scientists to positively impact people’s lives in new ways
through the integration of materials with AI. This talk will highlight
some examples where 3M is leveraging AI to create smart products for
the future.

Jamal Afridi is a Data Scientist at 3M. Jamal's research interests lie at
the intersection of computer vision, machine learning and information
theory. Jamal has been invited to present his research at various
national and international venues. In 2014, at the International
Conference on Pattern Recognition (ICPR), his research paper was selected
as one of the four nominees for the prestigious 'Best Industry Related
Paper Award (BIRPA)'. Before joining 3M, Jamal completed his PhD in
Computer Science at Michigan State University. His PhD thesis focused on
Deep Learning algorithms and how they can be used for analyzing 3D MRI
data. His research efforts helped secure NIH R01 grant. Jamal continues
to review new research in machine learning and computer vision for
several well-known conferences and for the journal Pattern Recognition.

American Family increasingly uses machine learning and artificial
intelligence to drive its insurance business, but is also cultivating out
new data sources and methods to develop entirely new business models
related to financial services, property and risk. This talk will outline
our portfolio of work and showcase projects of particular interest to
machine learning practitioners.

Derrick Higgins is an R&D strategist, manager, data scientist and
computational linguist; since 2016, he has led American Family’s DSAL
data science team in Chicago. Prior to joining American Family, Dr.
Higgins was lead data scientist at Civis Analytics, and used deep
learning to uncover latent factors in political discussions on social
media. Before that, he was the director of NLP and speech research at the
Educational Testing Service, where he and his team developed tools for
analyzing student responses that are now used in leading testing programs
around the world, including the GRE® and TOEFL®. Dr. Higgins earned
a Ph.D. in Linguistics from the University of Chicago in 2002. He has
contributed to research in many fields of natural language processing and
educational measurement, including semantic representation, discourse
structure analysis, item generation, off-topic essay identification, and
the automated scoring of spoken responses. His research has been
published in leading conferences and journals in the fields of
computational linguistics, speech processing, and language testing, and
has resulted in ten patents.

Categorical models are a natural fit for many problems. When learning the
distribution of categories from samples, high-dimensionality may dilute the
data. Minimax optimality is too pessimistic to remedy this issue. A
serendipitously discovered estimator, absolute discounting, corrects
empirical frequencies by subtracting a constant from observed categories,
which it then redistributes among the unobserved. It outperforms classical
estimators empirically and has been used extensively in natural language
modeling. We'll rigorously explain the prowess of this estimator using less
pessimistic notions. We show that (1) absolute discounting recovers
classical minimax KL-risk rates and that (2) it is *adaptive* to an
effective dimension rather than the true dimension, thus the same estimator
can be used in all dimensions. We explore the practical implications with
an application to the Global Terrorism Database.

Mesrob I. Ohannessian is a Research Assistant Professor at the Toyota
Technological Institute at Chicago. He was previously a postdoc at UCSD,
MSR-Inria, and Université Paris-Sud. He received his PhD in EECS from MIT.
He has a passion for teaching at all levels. His research interests are in
machine learning, statistics, information theory, and their applications,
particularly to problems marked by data scarcity.

Modeling of ambiguity in data is important for applications where a single correct result does not exist,
such as image captioning, image inpainting and even machine translation. Impressive advances have been
reported in recent years, demonstrating that probability distributions or sampling over complex domains
can be learned by transforming classical distributions via deep nets, which are trained via a
saddle-point formulation. Optimization of this generative adversarial net formulation is however
challenging and many tricks have proven indispensable, some theoretically justified and others
empirically validated. In this talk we discuss promising findings based on an adversarial net formulation
which uses the sliced Wasserstein distance. It results in stable and fast training. We will show results
of generators which sample images of size 1024 x 1024 and are directly trained from scratch, i.e., no
sequential or hierarchical training process is necessary.

Alex Schwing is an Assistant Professor at the University of Illinois at Urbana-Champaign working with
talented students on computer vision and machine learning topics. He received his B.S. and diploma in
Electrical Engineering and Information Technology from Technical University of Munich in 2006 and 2008
respectively, and obtained a PhD in Computer Science from ETH Zurich in 2014. His PhD thesis was awarded
an ETH medal. Afterwards he joined University of Toronto as a postdoctoral fellow before joining UIUC in
2016. His research interests are in the area of computer vision and machine learning, where he has
co-authored numerous papers on topics in scene understanding, inference and learning algorithms, deep
learning, image and language processing and generative modeling.

Title.
Sample-Efficient Reinforcement Learning with Rich Observations.
(slides)Chair.
Mike Franklin
[+-
Abstract
]
We study a version of reinforcement learning in which the agent must learn how to choose actions based on
observations so as to maximize long-term reward. We focus especially on when the observations may be
realistically rich, such as images, text documents, patient records, etc. We introduce a new algorithm
for systematic exploration, in other words, for discovering through experimentation how best to choose
actions. Along the way, we also propose a new measure called the “Bellman rank” which we argue captures
the degree to which the learning problem exhibits underlying structure, and which can be favorably bounded
in a number of previously studied cases. We show that the Bellman rank determines the statistical
efficiency of our algorithm, which, although not computationally efficient, requires a number of samples
that is polynomial in the Bellman rank as well as more standard parameters, but which is entirely
independent of the size of the observation space.

This work is joint with Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, and John Langford.

[+-
Bio
]
Robert Schapire is a Principal Researcher at Microsoft Research in New York City. He received his PhD
from MIT in 1991. After a short post-doc at Harvard, he joined the technical staff at AT&T Labs (formerly
AT&T Bell Laboratories) in 1991. In 2002, he became a Professor of Computer Science at Princeton
University. He joined Microsoft Research in 2014. His awards include the 1991 ACM Doctoral Dissertation
Award, the 2003 Gödel Prize, and the 2004 Kanelakkis Theory and Practice Award (both of the last two with
Yoav Freund). He is a fellow of the AAAI, and a member of both the National Academy of Engineering and
the National Academy of Sciences. His main research interest is in theoretical and applied machine
learning, with particular focus on boosting, online learning, game theory, and maximum entropy.

Thursday, June 7.

Title.
Inherent Trade-Offs in Algorithmic Fairness.
Chair.
Matus Telgarsky
[+-
Abstract
]
Recent discussion in the public sphere about classification by algorithms
has involved tension between competing notions of what it means for such a
classification to be fair to different groups. We consider several of the
key fairness conditions that lie at the heart of these debates, and discuss
recent research establishing inherent trade-offs between these conditions.
We also consider a variety of methods for promoting fairness and related
notions for classification and selection problems that involve sets rather
than just individuals. This talk is based on joint work with Sendhil
Mullainathan, Manish Raghavan, and Maithra Raghu.

[+-
Bio
]
Jon Kleinberg is the Tisch University Professor in the Departments of
Computer Science and Information Science at Cornell University. His
research focuses on issues at the interface of algorithms, networks, and
information, with an emphasis on the social and information networks that
underpin the Web and other on-line media. He is a member of the National
Academy of Sciences and the National Academy of Engineering, and the
recipient of research fellowships from the MacArthur, Packard, Simons, and
Sloan Foundations, as well as awards including the Harvey Prize, the
Nevanlinna Prize, the SIGKDD Innovation Award, and the ACM Prize in
Computing.

Deep learning has yielded a step function improvement at an array
of important problems ranging from computer vision to natural language
processing, and there is enormous excitement about its potential. However,
building practical applications powered by deep learning remains an
enormous challenge: the necessary expertise is scarce, the hardware
requirements can be prohibitive, and current software tools are immature
and limited in scope. In this talk, we will first describe how deep
learning workflows are supported by existing software tooling. We will then
describe several promising opportunities to drastically improve these
workflows via novel algorithmic and software solutions, including automated
hyperparameter optimization, efficient utilization of distributed resources
via performance models, and reproducible workflow management. This talk
draws on academic work done at CMU, Berkeley, and UCLA, as well as our
experiences at Determined AI, a startup that provides software to make deep
learning engineers dramatically more productive.

Evan R. Sparks is co-founder and CEO of Determined AI. While earning
his PhD in Computer Science in Berkeley's AMPLab, he contributed to the
design and implementation of much of the large-scale machine learning
ecosystem around Apache Spark, including MLlib and KeystoneML. Prior to
Berkeley, Evan worked in quantitative finance and web intelligence. He also
holds an AB in Computer Science from Dartmouth College.

Mixtures of Linear Regressions (MLR) is an important mixture model with
many applications.
In this model, each observation is generated from one of the several
unknown linear regression
components, where the identity of the generated component is also unknown.
Previous works
either assume strong assumptions on the data distribution or have high
complexity. This paper
proposes a fixed parameter tractable algorithm for the problem under
general conditions, which
achieves global convergence and the sample complexity scales nearly
linearly in the dimension. In
particular, different from previous works that require the data to be from
the standard Gaussian, the
algorithm allows the data from Gaussians with different covariances. When
the conditional number
of the covariances and the number of components are fixed, the algorithm
has nearly optimal sample
complexity N = ~O(d) as well as nearly optimal computational complexity
~O(Nd), where d is the
dimension of the data space. To the best of our knowledge, this approach
provides the first such
recovery guarantee for this general setting.

Yingyu Liang is an assistant professor in the Department of Computer
Sciences at the University of Wisconsin-Madison. His research interests are
providing theoretical analysis for machine learning models and designing
efficient algorithms with provable guarantees for applications. He received
a B.S. in 2008 and an M.S. in 2010 in Computer Science from Tsinghua
University, and a Ph.D. degree in Computer Science from Georgia Institute
of Technology in 2014. He was a postdoctoral researcher in 2014-2017 in the
Computer Science Department at Princeton University.

One of the important problems in the field of distributed optimization is the problem of minimizing a sum
of local convex objective functions over a networked system. Most of existing work in this area focuses
on developing distributed algorithms in a synchronous setting under the presence of a central clock,
where the agents need to wait for the slowest one to finish the update, before proceeding to the next
iterate. Asynchronous distributed algorithms remove the need for a central coordinator, reduce the
synchronization wait, and allow some agents to compute faster and execute more iterations. In the
asynchronous setting, the only known algorithms for solving this problem could achieve either linear or
sublinear rate of convergence. In this work, we built upon the existing literature to develop and analyze
an asynchronous Newton-based method to solve a penalized version of the problem. We show that this
algorithm guarantees almost sure convergence with global linear and local quadratic rate in expectation.
Numerical studies confirm superior performance of our algorithm against other asynchronous methods.

Ermin Wei is currently an Assistant Professor at the EECS Dept of
Northwestern University. She completed her PhD studies in Electrical
Engineering and Computer Science at MIT in 2014, advised by Professor Asu
Ozdaglar, where she also obtained her M.S.. She received her
undergraduate triple degree in Computer Engineering, Finance and
Mathematics with a minor in German, from University of Maryland, College
Park. Wei has received many awards, including the Graduate Women of
Excellence Award, second place prize in Ernst A. Guillemen Thesis Award
and Alpha Lambda Delta National Academic Honor Society Betty Jo Budson
Fellowship. Wei's research interests include distributed optimization
methods, convex optimization and analysis, smart grid, communication
systems and energy networks and market economic analysis.

We present a recent line of work on estimating differential
networks and conducting statistical inference about parameters in a
high-dimensional setting. First, we consider a Gaussian setting and
show how to directly learn the difference between the graph
structures. A debiasing procedure will be presented for construction
of an asymptotically normal estimator of the difference. Next,
building on the first part, we show how to learn the difference
between two graphical models with latent variables. Linear convergence
rate is established for an alternating gradient descent procedure with
correct initialization. Simulation studies illustrate performance of
the procedure. We also illustrate the procedure on an application in
neuroscience. Finally, we will discuss how to do statistical inference
on the differential networks when data are not Gaussian.

Mladen Kolar is Assistant Professor of Econometrics and Statistics at the
University of Chicago Booth School of Business. His research is focused
on high-dimensional statistical methods, graphical models,
varying-coefficient models and data mining, driven by the need to uncover
interesting and scientifically meaningful structures from observational
data. Particular applications arise in studies of dynamic regulatory
networks and social media analysis. His research has appeared in several
publications including the Journal of Machine Learning Research, Annals
of Applied Statistics, and the Electronic Journal of Statistics. He also
regularly presents his research at the top machine learning conferences,
including Advances in Neural Information Processing Systems and the
International Conference of Machine Learning.

Kolar was awarded a prestigious Facebook Fellowship in 2010 for his work on
machine learning and network models. He spent a summer with Facebook’s ads
optimization team working on a large scale system for click-through rate
prediction. His other past research included work with INRIA Rocquencourt in
Paris, France and Joint Research Center in Ispra, Italy.

Kolar earned his PhD in Machine Learning in 2013 from Carnegie Mellon
University, as well as a diploma in Computer Engineering from the University of
Zagreb. For his Ph.D. thesis work on “Uncovering Structure in High-Dimensions:
Networks and Multi-task Learning Problems,” Kolar received from 2014 SIGKDD
Dissertation Award honorable mention.

Inverting OLAP is a popular paradigm for business data analysis.
In this paradigm analysts specify measures and multi-dimensional points or
sub-regions in a data set, and the system returns the value of the
specified metrics in the specified region. In this talk we consider
inverting this process - that is, rather than the analyst telling the
system where to look, the system tells the analyst where to look. We will
describe some problems that arise in this approach.

“Text creatives” are the snippets of text
that appear in search ads. We will briefly and at a high level discuss how
text classification models can be used to understand user queries and text
creatives and specifically how TF-HUB can be useful in text classification
tasks with small amount of data to improve the relevance and quality of ads
served.

Jeff Naughton is a Principal Scientist and Site Lead at Google-Madison.
Prior to February 2016 he was a professor with a research focus on database
management systems at the University of Wisconsin-Madison, where he held
the John P. Morgridge Chair of Computer Sciences. He received his PhD in
Computer Science from Stanford University.Xi Wu is a software engineer at
Google. He received his PhD in Computer Science from the University of
Wisconsin-Madison. He received a Google PhD Fellowship in 2016 in the area
of privacy and security.Kazoo Sone is a software engineer for Google. Since
he joined in 2011, he has led several machine learning & natural language
processing projects for AdWords. His current research interests are weakly
supervised learning & transfer learning for text modeling. He earned his
Ph.D in Aeronautics with minors in Applied Math & Computer Science from
Caltech.

Title.
Challenges and Opportunities for Data Science.
Chair.
Mike Franklin
[+-
Abstract
]
How do we draw sound and defensible conclusions from big data? This question lies at the heart of data
science. In this talk I will first describe some of the challenges and opportunities inherent in this
rapidly emerging field, and then discuss the current state of the art in one area of particular relevance
to Sandia National Laboratories: big network data. Progress in this area includes the development of new
large-sample theory that helps us to view and interpret networks as statistical data objects, along with
the transformation of this theory into new statistical methods to model and draw inferences from network
data in the real world. The insights that result from connecting theory to practice also feed back into
pure mathematics and theoretical computer science, prompting new questions at the interface of
combinatorics, analysis, probability, and algorithms.

[+-
Bio
]
Patrick J. Wolfe (SM) received B.S.E.E. and B.Mus. degrees from the
University of Illinois at Urbana-Champaign (1998) and his Ph.D. from the
University of Cambridge (2003) as U.S. National Science Foundation Graduate
Research Fellow. After teaching at Cambridge from 2001–2003, he joined the
faculty of Harvard University (2004) and received the Presidential Early
Career Award for Scientists and Engineers from the White House (2008). In
2012, he returned to the UK to take up an Established Career Fellowship in
the Mathematical Sciences at University College London (UCL), where he also
served as a Royal Society Research Fellow and as founding Executive
Director of UCL’s Big Data Institute. In 2017, he was appointed the
Frederick L. Hovde Dean of Science at Purdue University.

Dr. Wolfe is also a trustee and non-executive director of the Alan Turing
Institute, the U.K.’s National Institute for Data Science, and serves on the
board of its commercial subsidiary. Previously the Institute’s Deputy Director
and recently named its first honorary fellow, he played a leading role in
establishing the institute and shaping its priorities through an extensive
program of engagement with a diverse range of experts and stakeholders. He has
provided expert advice on applications of data science to policy, societal, and
commercial challenges, including to the U.S. and U.K. governments and to
a range of public and private bodies—including most recently the U.K. Food
Standards Agency as an inaugural member of its Science Council. Dr. Wolfe is
currently Chair, IEEE SPS Big Data Special Interest Group and serves on the
steering committee of the IEEE SPS Data Science Initiative, as well as
Co-Chair, Data Science Section of the Institute for Mathematical Statistics.

Dr. Wolfe has received awards for his research from a number of international
bodies, including the Royal Society, the Acoustical Society of America, and the
IEEE. He is active in the global mathematics, statistics, and physical sciences
communities, and most recently was an organizer and Simons Foundation Fellow at
the Isaac Newton Institute for Mathematical Sciences 2016 semester research
program on Theoretical Foundations for Statistical Network Analysis.

2:30-3 pm

Coffee break

3-4 pm

Panel: MMLS advisory board

4-4:10 pm

Closing remarks

Panelists.

Ben Zhao (moderator; UChicago).

Avrim Blum (TTI-C).

Laura Balzano (UMich).

Misha Belkin (OSU).

Jeff Naughton (Google).

Rob Nowak (UW-Madison).

Nati Srebro (TTI-C).

Grace Wahba (UW-Madison).

Sponsors.

The Midwest ML Symposium is only possible thanks to extremely generous gifts from the following sponsors!